STOPPING_RULE = 'max_eval'
MAX_EVAL = 400
MUTATION_RATE = 0.1
MUTATION_U = 0.
MUTATION_ST = 0.2
REF = [1., 1.]
# Load theoritical values
theo = PROBLEM.solutions()
pop = GOMORS(problem=PROBLEM,
dim=DIMENSION,
size=POP_SIZE,
n_objs=N_OBJS,
max_generation=MAX_GENERATION,
max_episode=MAX_EPISODE,
reference=REF,
minimize=MINIMIZE,
stopping_rule=STOPPING_RULE,
max_eval=MAX_EVAL,
revolution=False)
pop.selection_fun = pop.compute_front
pop.mutation_fun = gaussian_mutator
pop.crossover_fun = multiroutine_crossover(
routines=[nsga_crossover, random_crossover], ns=[1., 1.])
pop.embedded_ea = NSGA2
pop.mutaton_rate = MUTATION_RATE
# Parametrization
params_ea = {
'u': MUTATION_U,
DIMENSION = 30
POP_SIZE = 64
N_OBJS = 2
MAX_GENERATION = 30
MAX_EPISODE = 120
STOPPING_RULE = 'max_eval'
MUTATION_RATE = 0.1
MUTATION_U = 0.
MUTATION_ST = 0.2
REF = [1., 1.]
pop = GOMORS(dim=DIMENSION,
size=POP_SIZE,
n_objs=N_OBJS,
fitness_fun=PM_FUN,
max_generation=MAX_GENERATION,
max_episode=MAX_EPISODE,
reference=REF,
minimize=MINIMIZE,
stopping_rule=STOPPING_RULE)
pop.selection_fun = pop.compute_front
pop.mutation_fun = gaussian_mutator
pop.bounds.append([0., 1.])
pop.crossover_fun = nsga_crossover
pop.mutaton_rate = MUTATION_RATE
pop.verbose = True
# Parametrization
region = 0
params_ea = {
'u': MUTATION_U,
def run_sim(n, path, simtitle, REVOLUTION, PROBLEM, seed):
POP_SIZE = 100
MAX_GENERATION = 50
MAX_EPISODE = 100
MAX_EVAL = 1000
STOPPING_RULE = 'max_eval'
MUTATION_RATE = 0.1
MUTATION_U = 0.
MUTATION_ST = 0.2
REF = [-14.3, 0.05]
MINIMIZE = True
VERBOSE = True
X_SCALER = StandardScaler()
# Set global numpy random_state
np.random.seed(seed)
theo = np.genfromtxt('pareto_Kursawe.txt', dtype='f8')
# Instantiate a population
pop = GOMORS(size=POP_SIZE,
problem=PROBLEM,
max_generation=MAX_GENERATION,
max_episode=MAX_EPISODE,
reference=REF,
minimize=MINIMIZE,
stopping_rule=STOPPING_RULE,
max_eval=MAX_EVAL,
mutation_rate=MUTATION_RATE,
revolution=REVOLUTION,
embedded_ea=MOEAD,
verbose=VERBOSE,
no_improvement_step_tol=4)
pop.selection_fun = pop.compute_front
pop.mutation_fun = gaussian_mutator
pop.crossover_fun = random_crossover
# Parametrization
params_ea = {
'u': MUTATION_U,
'st': MUTATION_ST,
'trial_method': 'lhs',
'trial_criterion': 'cm'
}
kernel = CubicKernel
tail = LinearTail
params_surrogate = \
{'kernel': kernel,
'tail': tail,
'maxp': MAX_EVAL + POP_SIZE,
'eta': 1e-8,
}
# ===============================Initialization============================
pop.config_surrogate(typ='rbf',
params=params_surrogate,
n_process=1,
X_scaler=X_SCALER,
warm_start=True)
pop.config_gap_opt(at='least_crowded',
radius=0.1,
size=POP_SIZE,
max_generation=MAX_GENERATION,
selection_fun=None,
mutation_fun=None,
mutation_rate=None,
crossover_fun=random_crossover,
trial_method='lhs',
trial_criterion='cm',
u=0.,
st=0.2)
pop.config_sampling(methods='default',
sizes='default',
rate='default',
candidates='default')
pop.run(params_ea=params_ea, params_surrogate=params_surrogate, theo=theo)
# ============================= Save Results ================================ #
# path to save
directory = path + simtitle + '/' + str(n) + '/'
if not os.path.exists(directory):
os.makedirs(directory)
pop.render_features(pop.true_front).tofile(directory + 'xs.dat')
pop.render_targets(pop.true_front).tofile(directory + 'fs.dat')
np.array(pop.hypervol_diff).tofile(directory + 'hv_diff.dat')
np.array(pop.hypervol_cov).tofile(directory + 'hv_cov.dat')
np.array(pop.hypervol_index).tofile(directory + 'hv_ind.dat')
# ================================Visualization============================== #
# plot_res(pop=pop, ref=theo, directory=directory)
return pop
# Kursawe
PM_FUN = kursawe
MINIMIZE = True
DIMENSION = 3
N_OBJS = 2
POP_SIZE = 64
MAX_GENERATION = 40
MAX_EPISODE = 60
MUTATION_RATE = 0.1
MUTATION_U = 0.
MUTATION_ST = 0.7
REF = [-14., 0.1]
pop = GOMORS(dim=DIMENSION, size=POP_SIZE, n_objs=N_OBJS, fitness_fun=PM_FUN,
max_generation=MAX_GENERATION, max_episode=MAX_EPISODE,
reference=REF, minimize=MINIMIZE)
pop.mutation_fun = gaussian_mutator
pop.bounds.append([-5., 5.])
pop.crossover_fun = nsga_crossover
pop.mutaton_rate = MUTATION_RATE
# Parametrization
params_ea = {'u': MUTATION_U,
'st': MUTATION_ST,
'trial_method': 'lhs',
'trial_criterion': 'cm'}
params_surrogate = \
{'hidden_layer_sizes': (4, 8),
'activation': 'relu',
POP_SIZE = 64
N_OBJS = 2
MAX_GENERATION = 40
MAX_EPISODE = 50
STOPPING_RULE = 'max_eval'
MAX_EVAL = 2500
MUTATION_RATE = 0.1
MUTATION_U = 0.
MUTATION_ST = 0.2
REF = [1., 1.]
pop = GOMORS(dim=DIMENSION,
size=POP_SIZE,
n_objs=N_OBJS,
fitness_fun=PM_FUN,
max_generation=MAX_GENERATION,
max_episode=MAX_EPISODE,
reference=REF,
minimize=MINIMIZE,
stopping_rule=STOPPING_RULE,
max_eval=MAX_EVAL)
pop.selection_fun = pop.compute_front
pop.mutation_fun = gaussian_mutator
pop.bounds = np.repeat([[0., 1.], [-5., 5.]], [1, DIMENSION - 1], axis=0)
pop.crossover_fun = multiroutine_crossover(
routines=[nsga_crossover, random_crossover], ns=[1., 1.5])
pop.mutaton_rate = MUTATION_RATE
# Parametrization
params_ea = {
'u': MUTATION_U,
'st': MUTATION_ST,